An encoding \(h_{t^\prime }\)  thus represents knowledge contained in the transitions between the values over time and observation density.
These hidden states can therefore use patterns in values and observations to support both earliness and accuracy.
For instance, if observations are arriving rapidly, more may arrive soon, so waiting to receive them will not incur much penalty for waiting [1]}.
On the other hand, if the hidden states indicate low observation density, it may be better to cut our losses since more observations may be far in the future.
Alternatively, the time-since-last-observation can itself be a valuable feature [2]}, so waiting to see if more observations arrive can itself be a reasonable policy for accurate classification.
